Overview

Dataset statistics

Number of variables22
Number of observations8522
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory176.0 B

Variable types

Numeric21
Categorical1

Alerts

NumRadicalElectrons has constant value "0" Constant
df_index is highly correlated with NumRadicalElectronsHigh correlation
NumValenceElectrons is highly correlated with HeavyAtomCount and 5 other fieldsHigh correlation
HeavyAtomCount is highly correlated with NumValenceElectrons and 5 other fieldsHigh correlation
NHOHCount is highly correlated with NumHDonorsHigh correlation
NOCount is highly correlated with NumValenceElectrons and 4 other fieldsHigh correlation
NumAliphaticCarbocycles is highly correlated with NumAliphaticRings and 2 other fieldsHigh correlation
NumAromaticCarbocycles is highly correlated with NumAromaticRingsHigh correlation
NumAromaticHeterocycles is highly correlated with NumAromaticRings and 2 other fieldsHigh correlation
NumAliphaticHeterocycles is highly correlated with NumAliphaticRings and 2 other fieldsHigh correlation
NumAliphaticRings is highly correlated with NumAliphaticCarbocycles and 5 other fieldsHigh correlation
NumAromaticRings is highly correlated with NumAromaticCarbocycles and 2 other fieldsHigh correlation
NumHAcceptors is highly correlated with NumValenceElectrons and 4 other fieldsHigh correlation
NumHDonors is highly correlated with NHOHCountHigh correlation
NumHeteroatoms is highly correlated with NumValenceElectrons and 3 other fieldsHigh correlation
NumRotatableBonds is highly correlated with NumValenceElectrons and 1 other fieldsHigh correlation
NumSaturatedCarbocycles is highly correlated with NumAliphaticCarbocycles and 3 other fieldsHigh correlation
NumSaturatedHeterocycles is highly correlated with NumAliphaticHeterocycles and 2 other fieldsHigh correlation
NumSaturatedRings is highly correlated with NumAliphaticCarbocycles and 5 other fieldsHigh correlation
RingCount is highly correlated with NumValenceElectrons and 5 other fieldsHigh correlation
fr_Ar_N is highly correlated with NumAromaticHeterocycles and 1 other fieldsHigh correlation
fr_NH0 is highly correlated with NOCount and 3 other fieldsHigh correlation
NumRadicalElectrons is highly correlated with df_index and 20 other fieldsHigh correlation
df_index has unique values Unique
NHOHCount has 2458 (28.8%) zeros Zeros
NumAliphaticCarbocycles has 7276 (85.4%) zeros Zeros
NumAromaticCarbocycles has 1532 (18.0%) zeros Zeros
NumAromaticHeterocycles has 3177 (37.3%) zeros Zeros
NumAliphaticHeterocycles has 4776 (56.0%) zeros Zeros
NumAliphaticRings has 4105 (48.2%) zeros Zeros
NumAromaticRings has 604 (7.1%) zeros Zeros
NumHDonors has 2457 (28.8%) zeros Zeros
NumRotatableBonds has 174 (2.0%) zeros Zeros
NumSaturatedCarbocycles has 7697 (90.3%) zeros Zeros
NumSaturatedHeterocycles has 5797 (68.0%) zeros Zeros
NumSaturatedRings has 5305 (62.3%) zeros Zeros
RingCount has 172 (2.0%) zeros Zeros
fr_Ar_N has 3698 (43.4%) zeros Zeros
fr_NH0 has 1608 (18.9%) zeros Zeros

Reproduction

Analysis started2022-11-04 07:13:42.917659
Analysis finished2022-11-04 07:14:52.199803
Duration1 minute and 9.28 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct8522
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6220.515372
Minimum0
Maximum12664
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:52.318236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile591.05
Q13023.25
median6158
Q39418.5
95-th percentile11950.95
Maximum12664
Range12664
Interquartile range (IQR)6395.25

Descriptive statistics

Standard deviation3657.676603
Coefficient of variation (CV)0.5880021805
Kurtosis-1.216095087
Mean6220.515372
Median Absolute Deviation (MAD)3197
Skewness0.0250161244
Sum53011232
Variance13378598.13
MonotonicityNot monotonic
2022-11-04T08:14:52.461751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51241
 
< 0.1%
99061
 
< 0.1%
64211
 
< 0.1%
64021
 
< 0.1%
96611
 
< 0.1%
27441
 
< 0.1%
59491
 
< 0.1%
43091
 
< 0.1%
77601
 
< 0.1%
23171
 
< 0.1%
Other values (8512)8512
99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
31
< 0.1%
81
< 0.1%
101
< 0.1%
121
< 0.1%
141
< 0.1%
151
< 0.1%
171
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
126641
< 0.1%
126631
< 0.1%
126611
< 0.1%
126601
< 0.1%
126591
< 0.1%
126581
< 0.1%
126571
< 0.1%
126561
< 0.1%
126541
< 0.1%
126531
< 0.1%

NumValenceElectrons
Real number (ℝ≥0)

HIGH CORRELATION

Distinct118
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.0330908
Minimum8
Maximum292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:52.605360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile74
Q1106
median126
Q3146
95-th percentile176
Maximum292
Range284
Interquartile range (IQR)40

Descriptive statistics

Standard deviation31.63374983
Coefficient of variation (CV)0.2509955887
Kurtosis0.4907922051
Mean126.0330908
Median Absolute Deviation (MAD)20
Skewness0.1288689652
Sum1074054
Variance1000.694128
MonotonicityNot monotonic
2022-11-04T08:14:52.728089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118242
 
2.8%
128231
 
2.7%
122225
 
2.6%
134224
 
2.6%
126222
 
2.6%
110221
 
2.6%
140221
 
2.6%
112218
 
2.6%
116215
 
2.5%
124214
 
2.5%
Other values (108)6289
73.8%
ValueCountFrequency (%)
82
 
< 0.1%
141
 
< 0.1%
241
 
< 0.1%
261
 
< 0.1%
281
 
< 0.1%
303
< 0.1%
321
 
< 0.1%
341
 
< 0.1%
367
0.1%
386
0.1%
ValueCountFrequency (%)
2921
 
< 0.1%
2722
 
< 0.1%
2621
 
< 0.1%
2602
 
< 0.1%
2561
 
< 0.1%
2542
 
< 0.1%
2503
< 0.1%
2461
 
< 0.1%
2441
 
< 0.1%
2386
0.1%

NumRadicalElectrons
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8522 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08522
100.0%

Length

2022-11-04T08:14:52.850690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:14:52.962973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08522
100.0%

Most occurring characters

ValueCountFrequency (%)
08522
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08522
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08522
100.0%

HeavyAtomCount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct51
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.15524525
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:53.054748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q120
median24
Q328
95-th percentile34
Maximum55
Range54
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1574385
Coefficient of variation (CV)0.2549110322
Kurtosis0.4338424686
Mean24.15524525
Median Absolute Deviation (MAD)4
Skewness0.007862023607
Sum205851
Variance37.91404888
MonotonicityNot monotonic
2022-11-04T08:14:53.193226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24570
 
6.7%
26551
 
6.5%
22548
 
6.4%
23534
 
6.3%
25524
 
6.1%
21512
 
6.0%
27489
 
5.7%
20459
 
5.4%
28441
 
5.2%
29428
 
5.0%
Other values (41)3466
40.7%
ValueCountFrequency (%)
12
 
< 0.1%
21
 
< 0.1%
42
 
< 0.1%
54
 
< 0.1%
610
 
0.1%
716
 
0.2%
820
 
0.2%
935
0.4%
1056
0.7%
1143
0.5%
ValueCountFrequency (%)
551
 
< 0.1%
531
 
< 0.1%
504
 
< 0.1%
493
 
< 0.1%
482
 
< 0.1%
471
 
< 0.1%
467
0.1%
453
 
< 0.1%
444
 
< 0.1%
4310
0.1%

NHOHCount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.256043182
Minimum0
Maximum10
Zeros2458
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:53.325294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.225502289
Coefficient of variation (CV)0.9756848377
Kurtosis3.497259858
Mean1.256043182
Median Absolute Deviation (MAD)1
Skewness1.4899086
Sum10704
Variance1.501855859
MonotonicityNot monotonic
2022-11-04T08:14:53.433519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13281
38.5%
02458
28.8%
21688
19.8%
3652
 
7.7%
4250
 
2.9%
5112
 
1.3%
652
 
0.6%
716
 
0.2%
811
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
02458
28.8%
13281
38.5%
21688
19.8%
3652
 
7.7%
4250
 
2.9%
5112
 
1.3%
652
 
0.6%
716
 
0.2%
811
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
91
 
< 0.1%
811
 
0.1%
716
 
0.2%
652
 
0.6%
5112
 
1.3%
4250
 
2.9%
3652
 
7.7%
21688
19.8%
13281
38.5%

NOCount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.461746069
Minimum0
Maximum15
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:53.549875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.022407895
Coefficient of variation (CV)0.3702859616
Kurtosis0.4127164824
Mean5.461746069
Median Absolute Deviation (MAD)1
Skewness0.4202397953
Sum46545
Variance4.090133695
MonotonicityNot monotonic
2022-11-04T08:14:53.645489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
51710
20.1%
61536
18.0%
41431
16.8%
71158
13.6%
3874
10.3%
8695
8.2%
2440
 
5.2%
9357
 
4.2%
10139
 
1.6%
172
 
0.8%
Other values (6)110
 
1.3%
ValueCountFrequency (%)
07
 
0.1%
172
 
0.8%
2440
 
5.2%
3874
10.3%
41431
16.8%
51710
20.1%
61536
18.0%
71158
13.6%
8695
8.2%
9357
 
4.2%
ValueCountFrequency (%)
154
 
< 0.1%
147
 
0.1%
1310
 
0.1%
1222
 
0.3%
1160
 
0.7%
10139
 
1.6%
9357
 
4.2%
8695
8.2%
71158
13.6%
61536
18.0%

NumAliphaticCarbocycles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2247125088
Minimum0
Maximum7
Zeros7276
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:53.747819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6630980644
Coefficient of variation (CV)2.950872953
Kurtosis19.39965591
Mean0.2247125088
Median Absolute Deviation (MAD)0
Skewness4.034303936
Sum1915
Variance0.439699043
MonotonicityNot monotonic
2022-11-04T08:14:53.829749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
07276
85.4%
1877
 
10.3%
2204
 
2.4%
495
 
1.1%
352
 
0.6%
515
 
0.2%
62
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
07276
85.4%
1877
 
10.3%
2204
 
2.4%
352
 
0.6%
495
 
1.1%
515
 
0.2%
62
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
62
 
< 0.1%
515
 
0.2%
495
 
1.1%
352
 
0.6%
2204
 
2.4%
1877
 
10.3%
07276
85.4%

NumAromaticCarbocycles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.330673551
Minimum0
Maximum6
Zeros1532
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:53.932484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8779638678
Coefficient of variation (CV)0.6597890724
Kurtosis-0.1395843453
Mean1.330673551
Median Absolute Deviation (MAD)1
Skewness0.2438943961
Sum11340
Variance0.7708205532
MonotonicityNot monotonic
2022-11-04T08:14:54.029722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13363
39.5%
22984
35.0%
01532
18.0%
3568
 
6.7%
471
 
0.8%
53
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
01532
18.0%
13363
39.5%
22984
35.0%
3568
 
6.7%
471
 
0.8%
53
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
53
 
< 0.1%
471
 
0.8%
3568
 
6.7%
22984
35.0%
13363
39.5%
01532
18.0%

NumAromaticHeterocycles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9290072753
Minimum0
Maximum5
Zeros3177
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:54.136325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8746081928
Coefficient of variation (CV)0.9414438574
Kurtosis-0.3380616858
Mean0.9290072753
Median Absolute Deviation (MAD)1
Skewness0.5958961531
Sum7917
Variance0.7649394909
MonotonicityNot monotonic
2022-11-04T08:14:54.236137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
03177
37.3%
13151
37.0%
21843
21.6%
3325
 
3.8%
425
 
0.3%
51
 
< 0.1%
ValueCountFrequency (%)
03177
37.3%
13151
37.0%
21843
21.6%
3325
 
3.8%
425
 
0.3%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
425
 
0.3%
3325
 
3.8%
21843
21.6%
13151
37.0%
03177
37.3%

NumAliphaticHeterocycles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6022060549
Minimum0
Maximum6
Zeros4776
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:54.327317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7872859055
Coefficient of variation (CV)1.307336416
Kurtosis1.702728474
Mean0.6022060549
Median Absolute Deviation (MAD)0
Skewness1.26706303
Sum5132
Variance0.6198190971
MonotonicityNot monotonic
2022-11-04T08:14:54.416255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04776
56.0%
12537
29.8%
21078
 
12.6%
395
 
1.1%
427
 
0.3%
58
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
04776
56.0%
12537
29.8%
21078
 
12.6%
395
 
1.1%
427
 
0.3%
58
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
58
 
0.1%
427
 
0.3%
395
 
1.1%
21078
 
12.6%
12537
29.8%
04776
56.0%

NumAliphaticRings
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8269185637
Minimum0
Maximum9
Zeros4105
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:54.535748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.040304141
Coefficient of variation (CV)1.258049083
Kurtosis4.394591789
Mean0.8269185637
Median Absolute Deviation (MAD)1
Skewness1.701312656
Sum7047
Variance1.082232705
MonotonicityNot monotonic
2022-11-04T08:14:54.640196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04105
48.2%
12637
30.9%
21276
 
15.0%
3274
 
3.2%
4150
 
1.8%
558
 
0.7%
615
 
0.2%
83
 
< 0.1%
72
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
04105
48.2%
12637
30.9%
21276
 
15.0%
3274
 
3.2%
4150
 
1.8%
558
 
0.7%
615
 
0.2%
72
 
< 0.1%
83
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
83
 
< 0.1%
72
 
< 0.1%
615
 
0.2%
558
 
0.7%
4150
 
1.8%
3274
 
3.2%
21276
 
15.0%
12637
30.9%
04105
48.2%

NumAromaticRings
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.259680826
Minimum0
Maximum7
Zeros604
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:54.745625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.160124874
Coefficient of variation (CV)0.5134020967
Kurtosis-0.3522425818
Mean2.259680826
Median Absolute Deviation (MAD)1
Skewness0.01304018421
Sum19257
Variance1.345889723
MonotonicityNot monotonic
2022-11-04T08:14:54.849688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
22866
33.6%
32287
26.8%
11519
17.8%
41100
 
12.9%
0604
 
7.1%
5132
 
1.5%
613
 
0.2%
71
 
< 0.1%
ValueCountFrequency (%)
0604
 
7.1%
11519
17.8%
22866
33.6%
32287
26.8%
41100
 
12.9%
5132
 
1.5%
613
 
0.2%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
613
 
0.2%
5132
 
1.5%
41100
 
12.9%
32287
26.8%
22866
33.6%
11519
17.8%
0604
 
7.1%

NumHAcceptors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.740553861
Minimum0
Maximum16
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:54.968722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.943919911
Coefficient of variation (CV)0.4100617708
Kurtosis0.2487370973
Mean4.740553861
Median Absolute Deviation (MAD)1
Skewness0.4808668791
Sum40399
Variance3.77882462
MonotonicityNot monotonic
2022-11-04T08:14:55.085402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
41822
21.4%
51525
17.9%
31446
17.0%
61210
14.2%
7777
9.1%
2743
8.7%
8504
 
5.9%
9201
 
2.4%
1191
 
2.2%
1054
 
0.6%
Other values (5)49
 
0.6%
ValueCountFrequency (%)
016
 
0.2%
1191
 
2.2%
2743
8.7%
31446
17.0%
41822
21.4%
51525
17.9%
61210
14.2%
7777
9.1%
8504
 
5.9%
9201
 
2.4%
ValueCountFrequency (%)
162
 
< 0.1%
142
 
< 0.1%
1212
 
0.1%
1117
 
0.2%
1054
 
0.6%
9201
 
2.4%
8504
 
5.9%
7777
9.1%
61210
14.2%
51525
17.9%

NumHDonors
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.137643746
Minimum0
Maximum8
Zeros2457
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:55.204960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.018635414
Coefficient of variation (CV)0.8953905104
Kurtosis1.760010137
Mean1.137643746
Median Absolute Deviation (MAD)1
Skewness1.043050025
Sum9695
Variance1.037618107
MonotonicityNot monotonic
2022-11-04T08:14:55.311792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
13507
41.2%
02457
28.8%
21771
20.8%
3575
 
6.7%
4160
 
1.9%
536
 
0.4%
613
 
0.2%
82
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
02457
28.8%
13507
41.2%
21771
20.8%
3575
 
6.7%
4160
 
1.9%
536
 
0.4%
613
 
0.2%
71
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
71
 
< 0.1%
613
 
0.2%
536
 
0.4%
4160
 
1.9%
3575
 
6.7%
21771
20.8%
13507
41.2%
02457
28.8%

NumHeteroatoms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.325627787
Minimum0
Maximum18
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:55.421140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q38
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.240587057
Coefficient of variation (CV)0.3542078561
Kurtosis0.4632880903
Mean6.325627787
Median Absolute Deviation (MAD)1
Skewness0.3994642361
Sum53907
Variance5.020230359
MonotonicityNot monotonic
2022-11-04T08:14:55.967559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
61543
18.1%
51382
16.2%
71351
15.9%
81065
12.5%
4988
11.6%
9709
8.3%
3533
 
6.3%
10373
 
4.4%
2240
 
2.8%
11166
 
1.9%
Other values (9)172
 
2.0%
ValueCountFrequency (%)
02
 
< 0.1%
133
 
0.4%
2240
 
2.8%
3533
 
6.3%
4988
11.6%
51382
16.2%
61543
18.1%
71351
15.9%
81065
12.5%
9709
8.3%
ValueCountFrequency (%)
183
 
< 0.1%
172
 
< 0.1%
164
 
< 0.1%
157
 
0.1%
1415
 
0.2%
1322
 
0.3%
1284
 
1.0%
11166
 
1.9%
10373
4.4%
9709
8.3%

NumRotatableBonds
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.38324337
Minimum0
Maximum21
Zeros174
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:56.084775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.260635185
Coefficient of variation (CV)0.5157448479
Kurtosis2.496012581
Mean4.38324337
Median Absolute Deviation (MAD)1
Skewness0.9577371823
Sum37354
Variance5.110471442
MonotonicityNot monotonic
2022-11-04T08:14:56.192254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
41709
20.1%
31518
17.8%
51434
16.8%
21051
12.3%
6923
10.8%
7602
 
7.1%
1409
 
4.8%
8302
 
3.5%
9177
 
2.1%
0174
 
2.0%
Other values (11)223
 
2.6%
ValueCountFrequency (%)
0174
 
2.0%
1409
 
4.8%
21051
12.3%
31518
17.8%
41709
20.1%
51434
16.8%
6923
10.8%
7602
 
7.1%
8302
 
3.5%
9177
 
2.1%
ValueCountFrequency (%)
211
 
< 0.1%
191
 
< 0.1%
183
 
< 0.1%
172
 
< 0.1%
167
 
0.1%
158
 
0.1%
144
 
< 0.1%
1315
 
0.2%
1222
0.3%
1147
0.6%

NumSaturatedCarbocycles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1525463506
Minimum0
Maximum6
Zeros7697
Zeros (%)90.3%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:56.304712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5512880591
Coefficient of variation (CV)3.613905261
Kurtosis26.12203376
Mean0.1525463506
Median Absolute Deviation (MAD)0
Skewness4.712719157
Sum1300
Variance0.3039185241
MonotonicityNot monotonic
2022-11-04T08:14:56.403365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
07697
90.3%
1538
 
6.3%
2168
 
2.0%
361
 
0.7%
448
 
0.6%
59
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
07697
90.3%
1538
 
6.3%
2168
 
2.0%
361
 
0.7%
448
 
0.6%
59
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
59
 
0.1%
448
 
0.6%
361
 
0.7%
2168
 
2.0%
1538
 
6.3%
07697
90.3%

NumSaturatedHeterocycles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.432292889
Minimum0
Maximum6
Zeros5797
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:56.508758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7058553534
Coefficient of variation (CV)1.632817405
Kurtosis2.510863834
Mean0.432292889
Median Absolute Deviation (MAD)0
Skewness1.619426102
Sum3684
Variance0.4982317799
MonotonicityNot monotonic
2022-11-04T08:14:56.603759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
05797
68.0%
11858
 
21.8%
2796
 
9.3%
354
 
0.6%
414
 
0.2%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
05797
68.0%
11858
 
21.8%
2796
 
9.3%
354
 
0.6%
414
 
0.2%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
52
 
< 0.1%
414
 
0.2%
354
 
0.6%
2796
 
9.3%
11858
 
21.8%
05797
68.0%

NumSaturatedRings
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5848392396
Minimum0
Maximum9
Zeros5305
Zeros (%)62.3%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:56.695869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9107039433
Coefficient of variation (CV)1.557186799
Kurtosis4.862347898
Mean0.5848392396
Median Absolute Deviation (MAD)0
Skewness1.912452117
Sum4984
Variance0.8293816723
MonotonicityNot monotonic
2022-11-04T08:14:56.769795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
05305
62.3%
11944
 
22.8%
2948
 
11.1%
3203
 
2.4%
490
 
1.1%
521
 
0.2%
69
 
0.1%
91
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
05305
62.3%
11944
 
22.8%
2948
 
11.1%
3203
 
2.4%
490
 
1.1%
521
 
0.2%
69
 
0.1%
71
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
71
 
< 0.1%
69
 
0.1%
521
 
0.2%
490
 
1.1%
3203
 
2.4%
2948
 
11.1%
11944
 
22.8%
05305
62.3%

RingCount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.08659939
Minimum0
Maximum11
Zeros172
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:56.856569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.239725508
Coefficient of variation (CV)0.4016476877
Kurtosis0.5662623963
Mean3.08659939
Median Absolute Deviation (MAD)1
Skewness0.07097975018
Sum26304
Variance1.536919335
MonotonicityNot monotonic
2022-11-04T08:14:56.942340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
32793
32.8%
42232
26.2%
21775
20.8%
5728
 
8.5%
1637
 
7.5%
0172
 
2.0%
6146
 
1.7%
723
 
0.3%
814
 
0.2%
111
 
< 0.1%
ValueCountFrequency (%)
0172
 
2.0%
1637
 
7.5%
21775
20.8%
32793
32.8%
42232
26.2%
5728
 
8.5%
6146
 
1.7%
723
 
0.3%
814
 
0.2%
101
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
101
 
< 0.1%
814
 
0.2%
723
 
0.3%
6146
 
1.7%
5728
 
8.5%
42232
26.2%
32793
32.8%
21775
20.8%
1637
 
7.5%

fr_Ar_N
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.35590237
Minimum0
Maximum8
Zeros3698
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:57.044943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.50083293
Coefficient of variation (CV)1.10688864
Kurtosis-0.3044632404
Mean1.35590237
Median Absolute Deviation (MAD)1
Skewness0.8356288165
Sum11555
Variance2.252499485
MonotonicityNot monotonic
2022-11-04T08:14:57.157560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
03698
43.4%
21668
19.6%
11327
 
15.6%
41028
 
12.1%
3638
 
7.5%
5125
 
1.5%
630
 
0.4%
85
 
0.1%
73
 
< 0.1%
ValueCountFrequency (%)
03698
43.4%
11327
 
15.6%
21668
19.6%
3638
 
7.5%
41028
 
12.1%
5125
 
1.5%
630
 
0.4%
73
 
< 0.1%
85
 
0.1%
ValueCountFrequency (%)
85
 
0.1%
73
 
< 0.1%
630
 
0.4%
5125
 
1.5%
41028
 
12.1%
3638
 
7.5%
21668
19.6%
11327
 
15.6%
03698
43.4%

fr_NH0
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.05573809
Minimum0
Maximum10
Zeros1608
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:14:57.281496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.569966792
Coefficient of variation (CV)0.7636998119
Kurtosis-0.1770778602
Mean2.05573809
Median Absolute Deviation (MAD)1
Skewness0.5599668485
Sum17519
Variance2.464795729
MonotonicityNot monotonic
2022-11-04T08:14:57.392788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22161
25.4%
11786
21.0%
01608
18.9%
31327
15.6%
4998
11.7%
5458
 
5.4%
6150
 
1.8%
728
 
0.3%
83
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
01608
18.9%
11786
21.0%
22161
25.4%
31327
15.6%
4998
11.7%
5458
 
5.4%
6150
 
1.8%
728
 
0.3%
83
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
92
 
< 0.1%
83
 
< 0.1%
728
 
0.3%
6150
 
1.8%
5458
 
5.4%
4998
11.7%
31327
15.6%
22161
25.4%
11786
21.0%

Interactions

2022-11-04T08:14:48.857422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:13:52.719520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:13:56.290702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:13:59.217305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:02.075779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:04.457963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:07.188496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:10.201206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:12.765404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:15.467720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:18.435608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:20.898281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:23.768269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:26.555147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:29.708742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:32.466104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:35.118675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-04T08:14:43.361156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:45.935093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:48.612848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:51.333294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:13:56.119077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:13:59.105425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:01.963629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:04.350015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:07.064974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:10.082071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:12.647536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:15.334057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:18.318139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:20.780965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:23.623653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:26.438186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:29.592854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:32.289272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:34.991060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:37.696829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:40.427490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:43.498889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:46.059364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:14:48.743418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-04T08:14:57.522691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-04T08:14:57.829384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-04T08:14:58.184783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-04T08:14:58.545506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-04T08:14:58.940715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-04T08:14:51.545360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-04T08:14:52.016345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexNumValenceElectronsNumRadicalElectronsHeavyAtomCountNHOHCountNOCountNumAliphaticCarbocyclesNumAromaticCarbocyclesNumAromaticHeterocyclesNumAliphaticHeterocyclesNumAliphaticRingsNumAromaticRingsNumHAcceptorsNumHDonorsNumHeteroatomsNumRotatableBondsNumSaturatedCarbocyclesNumSaturatedHeterocyclesNumSaturatedRingsRingCountfr_Ar_Nfr_NH0
051245009140000003143000001
18178124024180111127196000323
27753116022060111126074000345
333921620302601122252114011422
41761122022050102214064022302
56014126024020202224042011402
66047162032050312245054011612
762884809250010013250000131
81012138028061220146074101544
92286106021060120036064000344

Last rows

df_indexNumValenceElectronsNumRadicalElectronsHeavyAtomCountNHOHCountNOCountNumAliphaticCarbocyclesNumAromaticCarbocyclesNumAromaticHeterocyclesNumAliphaticHeterocyclesNumAliphaticRingsNumAromaticRingsNumHAcceptorsNumHDonorsNumHeteroatomsNumRotatableBondsNumSaturatedCarbocyclesNumSaturatedHeterocyclesNumSaturatedRingsRingCountfr_Ar_Nfr_NH0
8512461580015350100013355000100
85139434130026160300034165000301
851487311880350902111380119011424
85152006130026070130048084000444
851611103126025190201126194000302
8517876996019240120033253000321
85185247182038460420066265000644
85193835114020130100012134000101
852010980122022050010014094000112
85213473102020150110024153000211